Development and validation of a composite AI model for the diagnosis of levator ani muscle avulsion

Wu, Shuangyu; Ren, Yong; Lin, Xin; Huang, Zeping; Zheng, Zhuo; Zhang, Xinling

doi:10.1007/s00330-022-08754-y

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…This can be achieved by expanding on previous works performing such tasks. One such study collected data that consisted of both patients with and without a levator ani muscle avulsion diagnosis using transperineal ultrasound [ 7 ]. The images were collected at rest, during pelvic floor muscle contraction, and on Valsalva.…”

Section: Discussionmentioning

confidence: 99%

“…In order to qualify for surgical treatment, an assessment of the integrity and structure of the LAM must be conducted, and operation is performed only on individuals with a smaller risk of post-surgery complications. Such an assessment is conducted using pelvic floor imaging [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…The diagnosis of LAM avulsion can be performed through a medical imaging-based examination, which is subjected to challenges. While magnetic resonance imaging (MRI) provides exceptional spatial resolution and contrast, it is an expensive imaging modality that causes significant discomfort to many patients, such as claustrophobia and anxiety [ 7 ]. Ultrasound imaging is the most common diagnostic method for pelvic floor disorders, as it is still able to achieve a high contrast while maintaining a low cost and minimal discomfort for patients [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The reported segmentation result was about 0.66 in terms of Dice similarity coefficient. An automated framework to diagnose LAM avulsion from transperineal ultrasound images was developed in [ 7 ]. Convolutional neural network (CNN) models were used to predict LAM avulsion and an AUC of 0.86 was achieved based on the validation data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images

Rabbat,

Qureshi,

Hsu

et al. 2023

Bioengineering

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Levator ani muscle (LAM) avulsion is a common complication of vaginal childbirth and is linked to several pelvic floor disorders. Diagnosing and treating these conditions require imaging of the pelvic floor and examination of the obtained images, which is a time-consuming process subjected to operator variability. In our study, we proposed using deep learning (DL) to automate the segmentation of the LAM from 3D endovaginal ultrasound images (EVUS) to improve diagnostic accuracy and efficiency. Over one thousand images extracted from the 3D EVUS data of healthy subjects and patients with pelvic floor disorders were utilized for the automated LAM segmentation. A U-Net model was implemented, with Intersection over Union (IoU) and Dice metrics being used for model performance evaluation. The model achieved a mean Dice score of 0.86, demonstrating a better performance than existing works. The mean IoU was 0.76, indicative of a high degree of overlap between the automated and manual segmentation of the LAM. Three other models including Attention UNet, FD-UNet and Dense-UNet were also applied on the same images which showed comparable results. Our study demonstrated the feasibility and accuracy of using DL segmentation with U-Net architecture to automate LAM segmentation to reduce the time and resources required for manual segmentation of 3D EVUS images. The proposed method could become an important component in AI-based diagnostic tools, particularly in low socioeconomic regions where access to healthcare resources is limited. By improving the management of pelvic floor disorders, our approach may contribute to better patient outcomes in these underserved areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images

Rabbat,

Qureshi,

Hsu

et al. 2023

Bioengineering

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“…Publication years ranged from 2019 to 2023. Two studies used 3D US images [211,212], two 2D [213][214][215], two of them derived 2D images from a 3D/4D data set [214,215], and one did not specify the type of image [210].…”

Section: Pelvic Floormentioning

confidence: 99%

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Jost,

Kosian,

Jimenez Cruz

et al. 2023

JCM

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Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.

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Validation of a built-in software in automatically reconstructing the tomographic images of the levator ani muscle

Qu,

Wu,

Zhang

et al. 2023

Int Urogynecol J

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Development and validation of a composite AI model for the diagnosis of levator ani muscle avulsion

Cited by 7 publications

References 25 publications

Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images

Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Validation of a built-in software in automatically reconstructing the tomographic images of the levator ani muscle

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